EOLO: Deep Machine Learning Algorithm for Embedded Object Segmentation that Only Looks Once
AUTHORS
Longfei Zeng,Lakehead University, Smart Health FabLab, Department of Computer Science, Canada
Sabah Mohammed,Lakehead University, Smart Health FabLab, Department of Computer Science, Canada
ABSTRACT
In this paper, we introduce an anchor-free and single-shot instance segmentation method, which is conceptually simple with 3 independent branches, fully convolutional and can be used by easily embedding it into mobile and embedded devices. Our method, refer as EOLO, reformulates the instance segmentation problem as predicting semantic segmentation and distinguishing overlapping objects problem, through instance center classification and 4D distance regression on each pixel. Moreover, we propose one effective loss function to deal with sampling high-quality center of gravity examples and optimization for 4D distance regression, which can significantly improve the mAP performance. Without any bells and whistles, EOLO achieves 27.7% in mask mAP under IoU50 and reaches 30 FPS on 1080Ti GPU, with single-model and single-scale training/testing on the challenging COCO2017 dataset. For the first time, we show the different comprehension of instance segmentation in recent methods, in terms of both up-bottoms, down-ups, and direct-predict paradigms. Then we illustrate our model and present related experiments and results. We hope that the proposed EOLO framework can serve as a fundamental baseline for a single-shot instance segmentation task in Real-time Industrial Scenarios.
KEYWORDS
Deep machine learning, Image segmentation, Instance segmentation, Embedded platforms
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